IJMLC 2016 Vol.6(4): 231-234 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2016.6.4.603

Semi-nonnegative Matrix Factorization Algorithm Based on Genetic Algorithm Initialization

M. Chouh and K. Boukhetala

Abstract—Semi-nonnegative matrix factorization (Semi-NMF) is one of variations of nonnegative matrix factorization model (NMF) when the data matrix X is unconstrained (it may have mixed signs). Semi-NMF decomposes X into two matrices A and B of dimensions n×k and k × p respectively, where each element of the matrix B is nonnegative, such that: X ≈ AB . In the present paper, we proposed a semi-nonnegative matrix factorization algorithm based on genetic algorithm (GA) initialization which has larger searching area and gives the best initialization for the Semi-NMF algorithm to get the optimal solution of semi-nonnegative matrix factorization problem. Also, we compared this initialization for Semi-NMF algorithm with both the random and the k-means initializations introduced in the literature.

Index Terms—Semi-nonnegative matrix factorization, genetic algorithm, initialization.

The authors are with Faculté de mathématiques, USTHB, El-Alia BP 32, Bab-Ezzouar 16111, Alger, Algérie (merich_88@hotmail.com, kboukhetala@usthb.dz).

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Cite: M. Chouh and K. Boukhetala, "Semi-nonnegative Matrix Factorization Algorithm Based on Genetic Algorithm Initialization," International Journal of Machine Learning and Computing vol. 6, no. 4, pp. 231-234, 2016.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net